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Scalable communication between agents

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Registers an R function to be called upon garbage collection of object or (optionally) at the end of an R session.

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MCP server that interacts with Obsidian via the Obsidian rest API community plugin - MarkusPfundstein/mcp-obsidian

A detailed walkthrough of my current workflow for using LLms to build software, from brainstorming through planning and execution.

For AI shortcut enthusiasts, the tool that will help you stay up-to-date with all the available AI shortcuts has finally arrived. AI Store, developed by @__MIKL__, is the ultimate tool for exploring, downloading, and managing the latest AI projects within the RoutineHub community. With AI Store, you have instant access to a constantly growing database, directly from your device, without the need to search through multiple sources. AI Store AI Store allows users to view AI-based shortcuts, s

Transform websites into clean, LLM-ready text files

Generate llms.txt for any website

Search for code, files, and paths across half a million public GitHub repositories.

Imagine a detective who loses his memory every time he falls asleep. To solve cases, he develops an ingenious system: tattooing critical facts on his body and keeping a carefully organized set of Polaroid photos. Each time he wakes up, he can quickly rebuild his understanding by following his own documentation system. That's the plot of Memento, and it inspired how we solved a common problem with AI coding assistants. Dont Believe His Lies Memento GIFfrom Dont Believe His Lies GIFs Here's

Unlocking code assistance with local LLMs & GitHub Copilot! Discover R optimization & streamline your workflow.

In this article, I'll share the principles of LLM agent evaluation and you how to do it using DeepEval.

Patterns from our colleagues' work building with Generative AI

RAG combines LLMs with information retrieval systems. Explore top RAG tools and learn how to choose the best one for your specific use case.

"This thing is a tangled mess." I was relieved to hear the presenter say the words I was thinking. He had just finished walking me through a new AI agent, which I'm going to call Sherpa throughout this article. Sherpa was a proof of concept for a new AI agent their team had been working

Up to this point, we've covered agentic system architecture, how to organize your system into sub-agents and to build uniform mechanisms to standardize communication. Today we'll turn our attention to the tool layer and one of the most important aspects of agentic system design you'll need to consider: data retrieval. Data Retrieval and Agentic RAG

The article discusses creating uniform interaction models in modular agentic systems for effective request dispatching among agents and subagents.

The article looks at the benefits of modularity in agentic systems, enhancing clarity, maintainability, and reducing complexity.

RAG pipelines are the key to providing your LLM-powered apps with fresh, accurate data. In this guide, we explore best practices and antipatterns.

This article delves into the technical intricacies of Agentic AI Architecture, exploring its core components, key principles, development phases, technological integrations, applications, challenges, and future directions.

This guide outlines how to create efficient agentic systems by focusing on three layers: tools, reasoning, and action. Each layer presents unique challenges that can impact overall system performance.

The Automated Design of Agentic Systems (ADAS) is a unique approach in AI that enables the creation of agents capable of designing, testing, and refining themselves.